



'''simple docstring'''

import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


SCREAMING_SNAKE_CASE__     :						Dict	=  1_6
SCREAMING_SNAKE_CASE__     :						Dict	=  3_2






def 				a  (     UpperCamelCase_    :    Accelerator      ,					UpperCamelCase_    :    int = 16      ,					UpperCamelCase_    :    str = "bert-base-cased"			)       ->	Union[str, Any]:
		snake_case__	     =AutoTokenizer.from_pretrained(UpperCamelCase_			)
		snake_case__	     =load_dataset('glue'      ,					'mrpc'			)

		def tokenize_function(UpperCamelCase_    :    Optional[int]			):
				# max_length=None => use the model max length (it's actually the default)
				snake_case__	     =tokenizer(examples['sentence1']      ,					examples['sentence2']      ,					truncation=UpperCamelCase_      ,					max_length=UpperCamelCase_			)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		snake_case__	     =datasets.map(
		    UpperCamelCase_      ,					batched=UpperCamelCase_      ,					remove_columns=['idx', 'sentence1', 'sentence2']      ,					load_from_cache_file=UpperCamelCase_			)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		snake_case__	     =tokenized_datasets.rename_column('label'      ,					'labels'			)

		def collate_fn(UpperCamelCase_    :    Optional[int]			):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(UpperCamelCase_      ,					padding='max_length'      ,					max_length=128      ,					return_tensors='pt'			)
				return tokenizer.pad(UpperCamelCase_      ,					padding='longest'      ,					return_tensors='pt'			)

		# Instantiate dataloaders.
		snake_case__	     =DataLoader(
		    tokenized_datasets['train']      ,					shuffle=UpperCamelCase_      ,					collate_fn=UpperCamelCase_      ,					batch_size=UpperCamelCase_			)
		snake_case__	     =DataLoader(
		    tokenized_datasets['validation']      ,					shuffle=UpperCamelCase_      ,					collate_fn=UpperCamelCase_      ,					batch_size=UpperCamelCase_			)

		return train_dataloader, eval_dataloader






def 				a  (     UpperCamelCase_    :    Optional[Any]      ,					UpperCamelCase_    :    Optional[int]      ,					UpperCamelCase_    :    List[str]      ,					UpperCamelCase_    :    str			)       ->	Tuple:
		model.eval()
		snake_case__	     =0
		for step, batch in enumerate(UpperCamelCase_			):
				# We could avoid this line since we set the accelerator with `device_placement=True`.
				batch.to(accelerator.device			)
				with torch.no_grad():
						snake_case__	     =model(**UpperCamelCase_			)
				snake_case__	     =outputs.logits.argmax(dim=-1			)
				# It is slightly faster to call this once, than multiple times
				snake_case__     ,	snake_case__	     =accelerator.gather(
				    (predictions, batch['labels'])			)  # If we are in a multiprocess environment, the last batch has duplicates
				if accelerator.use_distributed:
						if step == len(UpperCamelCase_			) - 1:
								snake_case__	     =predictions[: len(eval_dataloader.dataset			) - samples_seen]
								snake_case__	     =references[: len(eval_dataloader.dataset			) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=UpperCamelCase_      ,					references=UpperCamelCase_      ,					)

		snake_case__	     =metric.compute()
		return eval_metric["accuracy"]






def 				a  (     UpperCamelCase_    :    int      ,					UpperCamelCase_    :    List[str]			)       ->	Dict:
		# Initialize accelerator
		snake_case__	     =Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		snake_case__	     =config['lr']
		snake_case__	     =int(config['num_epochs']			)
		snake_case__	     =int(config['seed']			)
		snake_case__	     =int(config['batch_size']			)
		snake_case__	     =args.model_name_or_path

		set_seed(UpperCamelCase_			)
		snake_case__     ,	snake_case__	     =get_dataloaders(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		snake_case__	     =AutoModelForSequenceClassification.from_pretrained(UpperCamelCase_      ,					return_dict=UpperCamelCase_			)

		# Instantiate optimizer
		snake_case__	     =(
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		snake_case__	     =optimizer_cls(params=model.parameters()      ,					lr=UpperCamelCase_			)

		if accelerator.state.deepspeed_plugin is not None:
				snake_case__	     =accelerator.state.deepspeed_plugin.deepspeed_config[
				    'gradient_accumulation_steps'
				]
		else:
				snake_case__	     =1
		snake_case__	     =(len(UpperCamelCase_			) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				snake_case__	     =get_linear_schedule_with_warmup(
				    optimizer=UpperCamelCase_      ,					num_warmup_steps=0      ,					num_training_steps=UpperCamelCase_      ,					)
		else:
				snake_case__	     =DummyScheduler(UpperCamelCase_      ,					total_num_steps=UpperCamelCase_      ,					warmup_num_steps=0			)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__     ,	snake_case__	     =accelerator.prepare(
		    UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)

		# We need to keep track of how many total steps we have iterated over
		snake_case__	     =0
		# We also need to keep track of the stating epoch so files are named properly
		snake_case__	     =0
		snake_case__	     =evaluate.load('glue'      ,					'mrpc'			)
		snake_case__	     =num_epochs

		if args.partial_train_epoch is not None:
				snake_case__	     =args.partial_train_epoch

		if args.resume_from_checkpoint:
				accelerator.load_state(args.resume_from_checkpoint			)
				snake_case__	     =args.resume_from_checkpoint.split('epoch_'			)[1]
				snake_case__	     =''
				for char in epoch_string:
						if char.isdigit():
								state_epoch_num += char
						else:
								break
				snake_case__	     =int(UpperCamelCase_			) + 1
				snake_case__	     =evaluation_loop(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
				accelerator.print('resumed checkpoint performance:'      ,					UpperCamelCase_			)
				accelerator.print('resumed checkpoint\'s scheduler\'s lr:'      ,					lr_scheduler.get_lr()[0]			)
				accelerator.print('resumed optimizers\'s lr:'      ,					optimizer.param_groups[0]['lr']			)
				with open(os.path.join(args.output_dir      ,					f"""state_{starting_epoch-1}.json"""			)      ,					'r'			) as f:
						snake_case__	     =json.load(UpperCamelCase_			)
						assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["lr"] == lr_scheduler.get_lr()[0]
						), "Scheduler learning rate mismatch, loading from checkpoint failed"
						assert (
						    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
						), "Optimizer learning rate mismatch, loading from checkpoint failed"
						assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
						return

    # Now we train the model
		snake_case__	     ={}
		for epoch in range(UpperCamelCase_      ,					UpperCamelCase_			):
				model.train()
				for step, batch in enumerate(UpperCamelCase_			):
						snake_case__	     =model(**UpperCamelCase_			)
						snake_case__	     =outputs.loss
						snake_case__	     =loss / gradient_accumulation_steps
						accelerator.backward(UpperCamelCase_			)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1
				snake_case__	     =f"""epoch_{epoch}"""
				snake_case__	     =os.path.join(args.output_dir      ,					UpperCamelCase_			)
				accelerator.save_state(UpperCamelCase_			)
				snake_case__	     =evaluation_loop(UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_      ,					UpperCamelCase_			)
				snake_case__	     =accuracy
				snake_case__	     =lr_scheduler.get_lr()[0]
				snake_case__	     =optimizer.param_groups[0]['lr']
				snake_case__	     =epoch
				snake_case__	     =overall_step
				accelerator.print(f"""epoch {epoch}:"""      ,					UpperCamelCase_			)

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
						with open(os.path.join(args.output_dir      ,					f"""state_{epoch}.json"""			)      ,					'w'			) as f:
								json.dump(UpperCamelCase_      ,					UpperCamelCase_			)






def 				a  (     )       ->	Union[str, Any]:
		snake_case__	     =argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'			)
		parser.add_argument(
		    '--model_name_or_path'      ,					type=UpperCamelCase_      ,					default='bert-base-cased'      ,					help='Path to pretrained model or model identifier from huggingface.co/models.'      ,					required=UpperCamelCase_      ,					)
		parser.add_argument(
		    '--output_dir'      ,					type=UpperCamelCase_      ,					default='.'      ,					help='Optional save directory where all checkpoint folders will be stored. Default is the current working directory.'      ,					)
		parser.add_argument(
		    '--resume_from_checkpoint'      ,					type=UpperCamelCase_      ,					default=UpperCamelCase_      ,					help='If the training should continue from a checkpoint folder.'      ,					)
		parser.add_argument(
		    '--partial_train_epoch'      ,					type=UpperCamelCase_      ,					default=UpperCamelCase_      ,					help='If passed, the training will stop after this number of epochs.'      ,					)
		parser.add_argument(
		    '--num_epochs'      ,					type=UpperCamelCase_      ,					default=2      ,					help='Number of train epochs.'      ,					)
		snake_case__	     =parser.parse_args()
		snake_case__	     ={'lr': 2e-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}

		training_function(UpperCamelCase_      ,					UpperCamelCase_			)


if __name__ == "__main__":
			main()









'''simple docstring'''

import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


SCREAMING_SNAKE_CASE__     :						int	=  get_tests_dir('''fixtures/test_sentencepiece_with_bytefallback.model''')

@require_sentencepiece
@require_tokenizers
class 							a__( snake_case__   ,      unittest.TestCase     ):
			a_	: List[Any]									=      GPTSwaTokenizer
			a_	: int									=      False
			a_	: List[Any]									=      True
			a_	: Any									=      False






			def 						_lowercase  (     self						)							->							str:
					super().setUp()

					# We have a SentencePiece fixture for testing
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase					,   eos_token='<unk>'					,   bos_token='<unk>'					,   pad_token='<unk>'						)

					tokenizer.save_pretrained(self.tmpdirname						)






			def 						_lowercase  (     self					,   _UpperCAmelCase						)							->							Any:
					snake_case__	     ='This is a test'
					snake_case__	     ='This is a test'
					return input_text, output_text






			def 						_lowercase  (     self						)							->							str:
					snake_case__	     ='<s>'
					snake_case__	     =1

					self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCAmelCase						)					,   _UpperCAmelCase						)
					self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =list(self.get_tokenizer().get_vocab().keys()						)

					self.assertEqual(vocab_keys[0]					,   '<unk>'						)
					self.assertEqual(vocab_keys[1]					,   '<s>'						)
					self.assertEqual(vocab_keys[-1]					,   'j'						)
					self.assertEqual(len(_UpperCAmelCase						)					,   2000						)






			def 						_lowercase  (     self						)							->							List[str]:
					self.assertEqual(self.get_tokenizer().vocab_size					,   2000						)






			def 						_lowercase  (     self						)							->							Any:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)

					snake_case__	     =tokenizer.tokenize('This is a test'						)
					self.assertListEqual(_UpperCAmelCase					,   ['▁This', '▁is', '▁a', '▁t', 'est']						)

					self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)					,   [465, 287, 265, 631, 842]						)

					snake_case__	     =tokenizer.tokenize('I was born in 92000, and this is falsé.'						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']					,   )
					# fmt: on

					snake_case__	     =tokenizer.convert_tokens_to_ids(_UpperCAmelCase						)
					self.assertListEqual(
					    _UpperCAmelCase					,   [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]					,   )

					snake_case__	     =tokenizer.convert_ids_to_tokens(_UpperCAmelCase						)
					# fmt: off
					self.assertListEqual(
					    _UpperCAmelCase					,   ['▁I', '▁was', '▁bor', 'n', '▁in', '▁', '<0x39>', '2', '0', '0', '0', ',', '▁and', '▁this', '▁is', '▁f', 'al', 's', '<0xC3>', '<0xA9>', '.']						)
					# fmt: on






			def 						_lowercase  (     self						)							->							Optional[Any]:
					snake_case__	     =GPTSwaTokenizer(_UpperCAmelCase						)
					snake_case__	     =['This is a test', 'I was born in 92000, and this is falsé.']
					snake_case__	     =[
					    [465, 287, 265, 631, 842],
					    [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
					]

					# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
					for text, expected_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertListEqual(tokenizer.encode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)

					# Test that decode_fast returns the input text
					for text, token_ids in zip(_UpperCAmelCase					,   _UpperCAmelCase						):
							self.assertEqual(tokenizer.decode_fast(_UpperCAmelCase						)					,   _UpperCAmelCase						)






			@slow
			def 						_lowercase  (     self						)							->							Dict:
					snake_case__	     =[
					    '<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')',
					    'Hey there, how are you doing this fine day?',
					    'This is a text with a trailing spaces followed by a dot     .',
					    'Häj sväjs lillebrör! =)',
					    'Det är inget fel på Mr. Cool',
					]

					# fmt: off
					snake_case__	     ={'input_ids': [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'token_type_ids': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
					# fmt: on
					self.tokenizer_integration_test_util(
					    expected_encoding=_UpperCAmelCase					,   model_name='AI-Sweden/gpt-sw3-126m'					,   sequences=_UpperCAmelCase					,   )




